The rise of digital game and simulation-based learning applications has led to new approaches in educational measurement that take account of patterns in time, high resolution paths of action, and clusters of virtual performance artifacts. The new approaches, which depart from traditional statistical analyses, include data mining, machine learning, and symbolic regression. This article briefly describes the context, methods and broad findings from two game-based analyses and describes key explanatory constructs use to make claims about the users, as well as the implications for design of digital game-based learning and assessment applications. [For the full proceedings, see ED562107.]